## PKU High-Dimensional Data Visualization - Galleries

### Dimension Reconstruction for Visual Exploration of Subspace Clusters in High-dimensional Data

In the projection, users can define a new dimension (like RD1 and RD2), where subspace clusters can be separated. Then, he can join the new dimensions in an original subspace (Subspace 3) to maintain the separation of data clusters.

### Dimension Projection-Matrix/Tree: Interactive Subspace Visual Exploration and Analysis of High Dimensional Data

In the dimension projection matrix (1), each cell is either a data projection (top right corner) or a dimension projection (bottom left corner) of a subspace. Rows and columns represent dimensions that constitute the subspaces.

The dimension projection tree: users can brush a group of data items (left) or dimensions (right) to create a child node containing the corresponding subspace.

### Visualization Assembly Line

### MLMD: Multi-Layered Visualization for Multi-Dimensional Data

### Multi-Dimensional Transfer Function Design based on Flexible Dimension Projection Embedded in Parallel Coordinates

### Multi-Dimensional Transfer Function Design based on Flexible Dimension Projection Embedded in Parallel Coordinates

### Interactive Local Clustering Operations In Parallel Coordinates

### Scattering Points in Parallel Coordinates

### Splatting the Lines in Parallel Coordinates

### Visual Clustering in Parallel Coordinates